Data Science Syllabus |

  1. Introduction to Data Science
  • This module explains what Data Science is and why companies use it.
  • You learn how data helps in business decision-making.
  • It covers basic terms like data, datasets, insights, analytics, and predictions.
  • You will also understand the roles in the data field—Data Analyst, Data Engineer, and Data Scientist.
  1. Python for Data Science
  • Python is the most popular programming language for data work.
  • You learn syntax, loops, functions, and data structures.
  • Libraries like NumPy, Pandas, and Matplotlib are introduced.
  • You start cleaning and analyzing data using real-world examples.
  1. Mathematics & Statistics for Data Science
  • Covers key math topics: algebra, probability, statistics, and distributions.
  • You learn mean, median, correlation, and statistical testing.
  • Helps you understand how to interpret numerical patterns in data.
  • This foundation is essential for machine learning algorithms.
  1. Data Collection & Data Wrangling
  • You learn how to collect raw data from CSV, Excel, web, and databases.
  • Data cleaning techniques like handling missing values, duplicates, and errors are taught.
  • You practice reshaping, merging, and filtering datasets.
  • The goal is to turn messy data into usable data.
  1. Data Visualization
  • You learn to create charts, graphs, and dashboards.
  • Tools: Matplotlib, Seaborn, Plotly, and Power BI/Tableau (optional).
  • You understand how to convert numbers into easy visuals.
  • Visualization helps communicate insights clearly to clients or teams.
  1. Exploratory Data Analysis (EDA)
  • EDA helps you understand the story hidden in the data.
  • You identify patterns, relationships, and unusual values.
  • Uses visual charts and statistical methods to analyze data.
  • EDA is the most important step before building ML models.
  1. SQL for Data Science
  • You learn how to store, retrieve, and filter data using SQL.
  • Covers SELECT, WHERE, GROUP BY, JOIN, and subqueries.
  • You practice working with real datasets in databases.
  • SQL helps you handle large datasets quickly and efficiently.
  1. Machine Learning (ML) Basics
  • Introduction to machine learning and its categories.
  • Covers supervised, unsupervised, and reinforcement learning.
  • Teaches core algorithms like Linear Regression, KNN, and Decision Trees.
  • You understand how machines learn from data to make predictions.
  1. Supervised Learning Algorithms
  • Algorithms that learn from labeled data (input + output).
  • Includes Logistic Regression, SVM, Random Forest, and Naive Bayes.
  • You learn when and why to use each algorithm.
  • Practical projects include classification and regression problems.
  1. Unsupervised Learning Algorithms
  • Works on data without labels—no predefined output.
  • Topics include clustering (K-Means) and dimensionality reduction (PCA).
  • Helps in grouping customers, segmenting markets, etc.
  • Useful for discovering hidden patterns in raw data.
  1. Deep Learning (Basics)
  • Introduction to neural networks and how the brain-inspired system learns.
  • Covers perceptron, activation functions, and hidden layers.
  • Tools: TensorFlow/Keras for building simple models.
  • You learn image and text basics using neural networks.
  1. Natural Language Processing (NLP)
  • Helps computers understand human language.
  • You learn tokenization, stemming, and sentiment analysis.
  • Explains how chatbots and text classification models work.
  • Real projects include analyzing reviews or social media comments.
  1. Big Data Basics
  • Introduction to large-scale data handling systems.
  • Covers Hadoop, Spark, and real-time data concepts.
  • You understand how major tech companies manage huge data.
  • Not deep coding—just the concepts for awareness.
  1. Data Science Projects
  • You work on end-to-end real-world projects.
  • Includes data collection, cleaning, EDA, ML model building, and testing.
  • Projects help you build confidence and practical experience.
  • These can be added to your resume and portfolio.
  1. Deployment & Model Optimization
  • Learn how to deploy your ML model into real applications.
  • Tools: Flask, FastAPI, Streamlit, Docker (intro).
  • You understand how to improve model accuracy and speed.
  • Deployment makes your model usable for real users.

 

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